code: def _disable_module_memory_sharing(data: Any) -> Any:
      """Disables memory sharing on parameters and buffers of `nn.Module`s contained in the given collection.
  
      Note: This is only required when running on CPU.
  
      """
      # PyTorch enables memory sharing automatically on all tensors that are passed through `mp.spawn`.
      # For model weights and buffers, this is undesired and can lead to race conditions between processes.
      # Hence, we copy the tensors in the entire module to ensure it doesn't share memory with other processes.
  
      @torch.no_grad()
      def unshare(module: Module) -> Module:
          for tensor in itertools.chain(
              module.parameters(), module.buffers()
          ):
              tensor.data = tensor.data.clone()
          return module
  
      return apply_to_collection(data, function=unshare, dtype=Module)
  
imports: import itertools
  import os
  from dataclasses import dataclass
  from multiprocessing.queues import SimpleQueue
  from textwrap import dedent
  from typing import TYPE_CHECKING, Any, Callable, Dict, Literal, Optional
  import torch
  import torch.backends.cudnn
  import torch.multiprocessing
  from lightning_utilities import apply_to_collection
  from torch.nn import Module
  from typing_extensions import override
  from lightning.fabric.accelerators.cpu import CPUAccelerator
  from lightning.fabric.strategies.launchers.launcher import _Launcher
  from lightning.fabric.utilities.apply_func import move_data_to_device
  from lightning.fabric.utilities.distributed import _set_num_threads_if_needed
  from lightning.fabric.utilities.imports import _IS_INTERACTIVE
  from lightning.fabric.utilities.seed import _collect_rng_states, _set_rng_states
label_desc: Use image node_id 2 for calling a global function with example usage: _disable_module_memory_sharing(data) and returns: apply_to_collection, module